Analysis overview
This systematic review and meta-analysis evaluated the effects of antipsychotic treatment on locomotor activity measured during social interaction tests in animal models. Effect sizes were calculated as Hedges’ g and synthesized using multilevel random-effects models to account for dependency between multiple outcomes within experiments and studies.
Study landscape and evidence distribution
Alluvial plot
Distribution of evidence across species, NMDA antagonists, and
antipsychotics. Alluvial plot illustrating how effect sizes are
distributed across animal species, NMDA receptor antagonists used to
induce social deficits, and antipsychotic drugs tested for reversal.
Evidence maps
Evidence maps of experimental design
characteristics.
Bubble size represents the number of effect sizes (k), and color
indicates the mean Hedges’ g within each cell.
Main meta-analysis
Overall effect
##
## Multivariate Meta-Analysis Model (k = 122; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.044 0.209 19 no study_id
## sigma^2.2 0.090 0.301 48 no study_id/exp_id
##
## Test for Heterogeneity:
## Q(df = 121) = 678.702, p-val < .001
##
## Number of estimates: 122
## Number of clusters: 19
## Estimates per cluster: 1-20 (mean: 6.42, median: 6)
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹ ci.ub¹
## -0.493 0.096 -5.133 13.06 <.001 -0.701 -0.286 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t-test and confidence interval, df: Satterthwaite approx)Multilevel random-effects meta-analysis with robust variance estimation.
Orchard plot
Overall effect on locomotor activity (social interaction test). Orchard plot summarizing study-level pooled effects with multilevel heterogeneity.
Prediction interval for the overall effect
## estimate ci_lb ci_ub pi_lb pi_ub
## 1 -0.4931826 -0.7006607 -0.2857045 -1.310629 0.324264Prediction interval for the overall effect. The 95% prediction interval reflects expected variability in the true effect size of a future study beyond sampling error.
## Component I.....
## 1 I2_Total 33.7
## 2 I2_study_id 11.0
## 3 I2_study_id/exp_id 22.7Multilevel heterogeneity estimates (I²).
Publication bias
Funnel plots
Funnel plot using standard error.
Funnel plot using inverse square root of total sample size.
Precision Effect Test (PET)
##
## Multivariate Meta-Analysis Model (k = 122; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.531 0.729 19 no study_id
## sigma^2.2 0.040 0.201 48 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 120) = 329.578, p-val < .001
##
## Number of estimates: 122
## Number of clusters: 19
## Estimates per cluster: 1-20 (mean: 6.42, median: 6)
##
## Test of Moderators (coefficient 2):¹
## F(df1 = 1, df2 = 6.05) = 51.459, p-val < .001
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹ ci.ub¹
## intrcpt 4.327 0.612 7.073 9.07 <.001 2.945 5.709 ***
## sqrt(vi) -9.256 1.290 -7.173 6.05 <.001 -12.407 -6.105 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)PET (Precision Effect Test) model with robust variance estimation. The PET model evaluates small-study bias by regressing effect size on study precision.
Precision Effect Estimate with Standard Error (PEESE)
##
## Multivariate Meta-Analysis Model (k = 122; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.063 0.251 19 no study_id
## sigma^2.2 0.000 0.000 48 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 120) = 390.292, p-val < .001
##
## Number of estimates: 122
## Number of clusters: 19
## Estimates per cluster: 1-20 (mean: 6.42, median: 6)
##
## Test of Moderators (coefficient 2):¹
## F(df1 = 1, df2 = 2.7) = 9.870, p-val = 0.059
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹ ci.ub¹
## intrcpt 0.563 0.333 1.691 8.71 0.126 -0.194 1.320
## vi -4.110 1.308 -3.142 2.7 0.059 -8.547 0.328 .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)PEESE (Precision Effect Estimate with Standard Error) model with robust variance estimation. The PEESE model provides an alternative bias-adjusted estimate using study variance.
Time-lag bias
##
## Multivariate Meta-Analysis Model (k = 122; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.051 0.225 19 no study_id
## sigma^2.2 0.095 0.308 48 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 120) = 678.120, p-val < .001
##
## Number of estimates: 122
## Number of clusters: 19
## Estimates per cluster: 1-20 (mean: 6.42, median: 6)
##
## Test of Moderators (coefficient 2):¹
## F(df1 = 1, df2 = 8.88) = 0.278, p-val = 0.611
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹ ci.ub¹
## intrcpt -0.506 0.104 -4.882 8.86 <.001 -0.741 -0.271 ***
## year_c 0.006 0.012 0.528 8.88 0.611 -0.021 0.034
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)Time-lag meta-regression model. This model tests whether effect sizes change systematically over publication time. A significant slope would indicate temporal trends such as decline or inflation of reported effects.
Time-lag bias: effect size as a function of publication year.
Moderators
Moderator analyses were conducted using multilevel meta-analytic models with robust variance estimation to examine whether effect sizes differed across experimental and biological characteristics. Orchard plots display pooled effects for each moderator level, with study-level clustering and multilevel heterogeneity taken into account.
##
## Multivariate Meta-Analysis Model (k = 122; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.137 0.370 19 no study_id
## sigma^2.2 0.135 0.368 48 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 113) = 669.383, p-val < .001
##
## Number of estimates: 122
## Number of clusters: 19
## Estimates per cluster: 1-20 (mean: 6.42, median: 6)
##
## Test of Moderators (coefficients 1:9):¹
## F(df1 = 9, df2 = 0) = 0.000, p-val = NA
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹
## atp_drugAripiprazole -0.827 0.706 -1.172 1.01 0.448 -9.593
## atp_drugCariprazine -1.244 0.170 -7.312 2.39 0.011 -1.872
## atp_drugChlorpromazine 0.025 0.434 0.058 1.15 0.962 -4.072
## atp_drugClozapine -0.299 0.213 -1.403 8.94 0.194 -0.782
## atp_drugHaloperidol -0.712 0.194 -3.663 5.59 0.012 -1.195
## atp_drugOlanzapine -0.454 0.130 -3.504 1.74 0.088 -1.098
## atp_drugQuetiapine -0.791 0.347 -2.280 1.25 0.221 -3.560
## atp_drugRisperidone -0.492 0.221 -2.230 4.98 0.076 -1.059
## atp_drugSulpiride -0.592 2.239 -0.264 1 0.835 -29.047
## ci.ub¹
## atp_drugAripiprazole 7.939
## atp_drugCariprazine -0.615 *
## atp_drugChlorpromazine 4.122
## atp_drugClozapine 0.184
## atp_drugHaloperidol -0.228 *
## atp_drugOlanzapine 0.190 .
## atp_drugQuetiapine 1.979
## atp_drugRisperidone 0.076 .
## atp_drugSulpiride 27.863
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)
##
## Multivariate Meta-Analysis Model (k = 122; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.061 0.247 19 no study_id
## sigma^2.2 0.090 0.300 48 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 120) = 678.174, p-val < .001
##
## Number of estimates: 122
## Number of clusters: 19
## Estimates per cluster: 1-20 (mean: 6.42, median: 6)
##
## Test of Moderators (coefficients 1:2):¹
## F(df1 = 2, df2 = 7.36) = 16.062, p-val = 0.002
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹
## atp_scheduleAcute -0.438 0.143 -3.064 10.49 0.011 -0.755
## atp_scheduleRepeated -0.575 0.102 -5.608 4.31 0.004 -0.851
## ci.ub¹
## atp_scheduleAcute -0.122 *
## atp_scheduleRepeated -0.298 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)
##
## Multivariate Meta-Analysis Model (k = 122; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.019 0.139 19 no study_id
## sigma^2.2 0.064 0.253 48 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 118) = 665.855, p-val < .001
##
## Number of estimates: 122
## Number of clusters: 19
## Estimates per cluster: 1-20 (mean: 6.42, median: 6)
##
## Test of Moderators (coefficients 1:4):¹
## F(df1 = 4, df2 = 2.4) = 4.153, p-val = 0.171
##
## Model Results:
##
## estimate se¹ tval¹ df¹
## atp_administration_routeIntraperitoneal -0.531 0.164 -3.241 5.7
## atp_administration_routeOral -0.287 0.281 -1.020 4.21
## atp_administration_routeSubcutaneous -0.473 0.118 -4.000 2.46
## atp_administration_routeUnclear -2.727 1.209 -2.257 1
## pval¹ ci.lb¹ ci.ub¹
## atp_administration_routeIntraperitoneal 0.019 -0.938 -0.125 *
## atp_administration_routeOral 0.363 -1.053 0.479
## atp_administration_routeSubcutaneous 0.040 -0.901 -0.045 *
## atp_administration_routeUnclear 0.266 -18.084 12.629
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)
##
## Multivariate Meta-Analysis Model (k = 122; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.054 0.232 19 no study_id
## sigma^2.2 0.085 0.291 48 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 119) = 672.955, p-val < .001
##
## Number of estimates: 122
## Number of clusters: 19
## Estimates per cluster: 1-20 (mean: 6.42, median: 6)
##
## Test of Moderators (coefficients 1:3):¹
## F(df1 = 3, df2 = 2.46) = 5.617, p-val = 0.122
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹
## nmda_antagonistKetamine -0.402 0.242 -1.663 1 0.344 -3.459
## nmda_antagonistMK-801 -0.253 0.151 -1.672 6.38 0.143 -0.618
## nmda_antagonistPhencyclidine -0.703 0.141 -4.998 5.25 0.004 -1.059
## ci.ub¹
## nmda_antagonistKetamine 2.655
## nmda_antagonistMK-801 0.112
## nmda_antagonistPhencyclidine -0.346 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)
##
## Multivariate Meta-Analysis Model (k = 122; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.048 0.218 19 no study_id
## sigma^2.2 0.095 0.308 48 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 120) = 677.599, p-val < .001
##
## Number of estimates: 122
## Number of clusters: 19
## Estimates per cluster: 1-20 (mean: 6.42, median: 6)
##
## Test of Moderators (coefficient 2):¹
## F(df1 = 1, df2 = 12.43) = 16.039, p-val = 0.002
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹
## intrcpt -0.111 0.000 -99554341690776.812 11.96 <.001 -0.111
## speciesRat -0.398 0.099 -4.005 12.43 0.002 -0.614
## ci.ub¹
## intrcpt -0.111 ***
## speciesRat -0.182 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)
##
## Multivariate Meta-Analysis Model (k = 122; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.097 0.312 19 no study_id
## sigma^2.2 0.082 0.287 48 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 119) = 678.568, p-val < .001
##
## Number of estimates: 122
## Number of clusters: 19
## Estimates per cluster: 1-20 (mean: 6.42, median: 6)
##
## Test of Moderators (coefficients 1:3):¹
## F(df1 = 3, df2 = 3.63) = 5.125, p-val = 0.084
##
## Model Results:
##
## estimate se¹ tval¹
## developmental_stage_inductionAdult -0.716 0.498 -1.439
## developmental_stage_inductionJuvenile/Adolescent -0.579 0.276 -2.095
## developmental_stage_inductionUnclear -0.436 0.105 -4.169
## df¹ pval¹ ci.lb¹
## developmental_stage_inductionAdult 1.63 0.312 -3.394
## developmental_stage_inductionJuvenile/Adolescent 2.95 0.129 -1.467
## developmental_stage_inductionUnclear 8.79 0.003 -0.673
## ci.ub¹
## developmental_stage_inductionAdult 1.961
## developmental_stage_inductionJuvenile/Adolescent 0.309
## developmental_stage_inductionUnclear -0.199 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)
##
## Multivariate Meta-Analysis Model (k = 122; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.355 0.596 19 no study_id
## sigma^2.2 0.115 0.339 48 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 106) = 657.999, p-val < .001
##
## Number of estimates: 122
## Number of clusters: 19
## Estimates per cluster: 1-20 (mean: 6.42, median: 6)
##
## Model Results:
##
## estimate se¹ tval¹
## atp_nmda_interactionClozapine × Ketamine -0.674 0.642 -1.050
## atp_nmda_interactionHaloperidol × Ketamine 0.151 0.374 0.403
## atp_nmda_interactionRisperidone × Ketamine 0.013 0.405 0.032
## atp_nmda_interactionAripiprazole × MK-801 -0.351 0.473 -0.741
## atp_nmda_interactionClozapine × MK-801 0.015 0.374 0.040
## atp_nmda_interactionHaloperidol × MK-801 -0.586 0.311 -1.881
## atp_nmda_interactionRisperidone × MK-801 -0.685 0.712 -0.962
## atp_nmda_interactionAripiprazole × Phencyclidine -2.021 0.000 NA
## atp_nmda_interactionCariprazine × Phencyclidine -1.434 0.323 -4.437
## atp_nmda_interactionChlorpromazine × Phencyclidine 0.045 0.298 0.149
## atp_nmda_interactionClozapine × Phencyclidine -0.601 0.458 -1.313
## atp_nmda_interactionHaloperidol × Phencyclidine -1.152 0.499 -2.311
## atp_nmda_interactionOlanzapine × Phencyclidine -0.509 0.256 -1.985
## atp_nmda_interactionQuetiapine × Phencyclidine -0.891 0.725 -1.229
## atp_nmda_interactionRisperidone × Phencyclidine -0.679 0.410 -1.656
## atp_nmda_interactionSulpiride × Phencyclidine -0.592 2.924 -0.202
## df¹ pval¹ ci.lb¹
## atp_nmda_interactionClozapine × Ketamine 1.01 0.484 -8.701
## atp_nmda_interactionHaloperidol × Ketamine 1.01 0.756 -4.538
## atp_nmda_interactionRisperidone × Ketamine 1 0.979 -5.116
## atp_nmda_interactionAripiprazole × MK-801 1 0.594 -6.366
## atp_nmda_interactionClozapine × MK-801 4.96 0.970 -0.950
## atp_nmda_interactionHaloperidol × MK-801 2.44 0.177 -1.720
## atp_nmda_interactionRisperidone × MK-801 1.08 0.503 -8.355
## atp_nmda_interactionAripiprazole × Phencyclidine NA NA NA
## atp_nmda_interactionCariprazine × Phencyclidine 2.58 0.029 -2.564
## atp_nmda_interactionChlorpromazine × Phencyclidine 1.78 0.897 -1.402
## atp_nmda_interactionClozapine × Phencyclidine 2.12 0.314 -2.469
## atp_nmda_interactionHaloperidol × Phencyclidine 2.07 0.143 -3.233
## atp_nmda_interactionOlanzapine × Phencyclidine 2.29 0.169 -1.487
## atp_nmda_interactionQuetiapine × Phencyclidine 1.88 0.351 -4.210
## atp_nmda_interactionRisperidone × Phencyclidine 3.16 0.192 -1.948
## atp_nmda_interactionSulpiride × Phencyclidine 1 0.873 -37.751
## ci.ub¹
## atp_nmda_interactionClozapine × Ketamine 7.353
## atp_nmda_interactionHaloperidol × Ketamine 4.840
## atp_nmda_interactionRisperidone × Ketamine 5.142
## atp_nmda_interactionAripiprazole × MK-801 5.665
## atp_nmda_interactionClozapine × MK-801 0.980
## atp_nmda_interactionHaloperidol × MK-801 0.549
## atp_nmda_interactionRisperidone × MK-801 6.984
## atp_nmda_interactionAripiprazole × Phencyclidine NA
## atp_nmda_interactionCariprazine × Phencyclidine -0.304 *
## atp_nmda_interactionChlorpromazine × Phencyclidine 1.491
## atp_nmda_interactionClozapine × Phencyclidine 1.268
## atp_nmda_interactionHaloperidol × Phencyclidine 0.929
## atp_nmda_interactionOlanzapine × Phencyclidine 0.469
## atp_nmda_interactionQuetiapine × Phencyclidine 2.428
## atp_nmda_interactionRisperidone × Phencyclidine 0.590
## atp_nmda_interactionSulpiride × Phencyclidine 36.567
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)
Meta-regression
Cumulative exposure
##
## Multivariate Meta-Analysis Model (k = 107; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.062 0.249 18 no study_id
## sigma^2.2 0.122 0.349 44 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 105) = 581.137, p-val < .001
##
## Number of estimates: 107
## Number of clusters: 18
## Estimates per cluster: 0-20 (mean: 5.63, median: 4)
##
## Test of Moderators (coefficient 2):¹
## F(df1 = 1, df2 = 1.15) = 25.431, p-val = 0.101
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹
## intrcpt -0.438 0.111 -3.947 12.8 0.002 -0.678
## atp_cumulative_exposure -0.010 0.002 -5.043 1.15 0.101 -0.029
## ci.ub¹
## intrcpt -0.198 **
## atp_cumulative_exposure 0.009
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)
Meta-regression of cumulative exposure versus effect size. The regression coefficient indicates whether increasing cumulative exposure is associated with changes in effect size, suggesting a potential dose–response relationship.
Log-transformed cumulative exposure
##
## Multivariate Meta-Analysis Model (k = 107; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.000 0.000 18 no study_id
## sigma^2.2 1.116 1.056 44 no study_id/exp_id
##
## Test for Residual Heterogeneity:
## QE(df = 105) = 507.602, p-val < .001
##
## Number of estimates: 107
## Number of clusters: 18
## Estimates per cluster: 0-20 (mean: 5.63, median: 4)
##
## Test of Moderators (coefficient 2):¹
## F(df1 = 1, df2 = 4.55) = 124.051, p-val < .001
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹
## intrcpt -0.837 0.150 -5.589 9.94 <.001 -1.172
## log_atp_cumulative_exposure -1.041 0.093 -11.138 4.55 <.001 -1.289
## ci.ub¹
## intrcpt -0.503 ***
## log_atp_cumulative_exposure -0.794 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t/F-tests and confidence intervals, df: Satterthwaite approx)
Meta-regression of log-transformed cumulative exposure. The log-transformed model evaluates potential non-linear exposure–effect relationships and the robustness of the association.
Sensitivity analyses
Rho sensitivity
## rho estimate ci
## 1 0.0 -0.76923519 [-0.922, -0.616]
## 2 0.3 -0.63134529 [-0.776, -0.487]
## 3 0.5 -0.49318260 [-0.682, -0.305]
## 4 0.8 -0.09590805 [-0.468, 0.276]
Sensitivity of the overall effect to within-study correlation (rho). This analysis evaluates the robustness of the pooled effect size to assumptions about the correlation between multiple effect sizes within the same experiment. Each row reports the overall effect estimate (Hedges’ g) and 95% confidence interval obtained under a different assumed value of rho. Stability of estimates across rho values indicates robustness to within-study dependency assumptions.
Leave-one-study-out
## left_out_study estimate ci_lb ci_ub
## 1 becker_2004 -0.4960334 -0.7139502 -0.2781166
## 2 corbett_1995 -0.4466817 -0.6054748 -0.2878886
## 3 gacsalyi_2013 -0.4881798 -0.6798151 -0.2965444
## 4 gururajan_2011 -0.4708840 -0.6676838 -0.2740842
## 5 gururajan_2012 -0.4830427 -0.6750026 -0.2910829
## 6 hereta_2019 -0.5026705 -0.7059116 -0.2994294
## 7 kaminska_2015 -0.5180835 -0.7072542 -0.3289127
## 8 maehara_2011 -0.5068092 -0.7001800 -0.3134384
## 9 morimoto_2002 -0.5326388 -0.6761006 -0.3891771
## 10 neill_2016 -0.4949793 -0.6918600 -0.2980986
## 11 pouzet_2002b -0.4597217 -0.6375125 -0.2819308
## 12 rung_2005b -0.5021678 -0.7036788 -0.3006569
## 13 sams-dodd_1996 -0.5164514 -0.7158479 -0.3170550
## 14 sams-dodd_1997 -0.4924735 -0.7112486 -0.2736983
## 15 sams-dodd_1998 -0.4962294 -0.7116213 -0.2808375
## 16 sams-dodd_1998d -0.4913187 -0.6899453 -0.2926920
## 17 satow_2009 -0.5124544 -0.7096829 -0.3152260
## 18 tarland_2017 -0.4648505 -0.6469680 -0.2827329
## 19 vijeepallam_2016 -0.5091156 -0.7039911 -0.3142400Leave-one-study-out analysis. Each row reports the pooled effect size (Hedges’ g) and 95% confidence interval obtained after excluding one study at a time from the meta-analysis. This analysis evaluates the influence of individual studies on the overall estimate; substantial changes after removal of a study would indicate disproportionate influence.
Excluding high risk of bias
##
## Multivariate Meta-Analysis Model (k = 83; method: REML)
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.050 0.224 10 no study_id
## sigma^2.2 0.105 0.324 25 no study_id/exp_id
##
## Test for Heterogeneity:
## Q(df = 82) = 551.351, p-val < .001
##
## Number of estimates: 83
## Number of clusters: 10
## Estimates per cluster: 0-20 (mean: 4.37, median: 2)
##
## Model Results:
##
## estimate se¹ tval¹ df¹ pval¹ ci.lb¹ ci.ub¹
## -0.409 0.130 -3.150 6.94 0.016 -0.717 -0.102 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## 1) results based on cluster-robust inference (var-cov estimator: CR2,
## approx t-test and confidence interval, df: Satterthwaite approx)
Overall effect excluding high risk-of-bias studies. This sensitivity analysis re-estimates the overall meta-analytic effect after excluding studies classified as having high risk of bias. The purpose of this analysis is to assess whether the pooled effect estimate is robust to the exclusion of potentially biased evidence.
Annex: Individual effect sizes included in the meta-analysis
Calculated effect sizes
##
## study effect_id species nmda_antagonist atp_drug hedges_g
## 1 Gururajan 2011 7 Rat MK-801 Clozapine -0.958
## 2 Gururajan 2011 8 Rat MK-801 Clozapine -1.387
## 3 Gururajan 2011 9 Rat MK-801 Clozapine -1.935
## 4 Gururajan 2011 13 Rat MK-801 Clozapine 0.400
## 5 Gururajan 2011 14 Rat MK-801 Clozapine -0.844
## 6 Gururajan 2011 15 Rat MK-801 Clozapine -0.869
## 7 Sams-Dodd 1997 33 Rat Phencyclidine Risperidone -0.272
## 8 Sams-Dodd 1997 39 Rat Phencyclidine Risperidone 0.765
## 9 Sams-Dodd 1997 34 Rat Phencyclidine Risperidone -1.590
## 10 Sams-Dodd 1997 40 Rat Phencyclidine Risperidone -0.765
## 11 Sams-Dodd 1997 57 Rat Phencyclidine Quetiapine -0.274
## 12 Sams-Dodd 1997 65 Rat Phencyclidine Quetiapine -0.406
## 13 Sams-Dodd 1997 46 Rat Phencyclidine Olanzapine -0.578
## 14 Sams-Dodd 1997 51 Rat Phencyclidine Olanzapine -0.095
## 15 Sams-Dodd 1997 35 Rat Phencyclidine Risperidone -3.189
## 16 Sams-Dodd 1997 41 Rat Phencyclidine Risperidone -1.907
## 17 Sams-Dodd 1997 58 Rat Phencyclidine Quetiapine -0.886
## 18 Sams-Dodd 1997 66 Rat Phencyclidine Quetiapine -0.931
## 19 Sams-Dodd 1997 47 Rat Phencyclidine Olanzapine -0.872
## 20 Sams-Dodd 1997 52 Rat Phencyclidine Olanzapine 0.069
## 21 Sams-Dodd 1997 53 Rat Phencyclidine Olanzapine -1.621
## 22 Sams-Dodd 1997 59 Rat Phencyclidine Quetiapine -0.809
## 23 Sams-Dodd 1997 67 Rat Phencyclidine Quetiapine -1.457
## 24 Sams-Dodd 1997 48 Rat Phencyclidine Olanzapine -4.463
## 25 Sams-Dodd 1997 60 Rat Phencyclidine Quetiapine -1.939
## 26 Sams-Dodd 1997 68 Rat Phencyclidine Quetiapine -1.546
## 27 Sams-Dodd 1998 95 Rat Phencyclidine Clozapine 0.153
## 28 Sams-Dodd 1998 73 Rat Phencyclidine Haloperidol -0.136
## 29 Sams-Dodd 1998 81 Rat Phencyclidine Haloperidol -0.277
## 30 Sams-Dodd 1998 89 Rat Phencyclidine Clozapine -0.256
## 31 Sams-Dodd 1998 96 Rat Phencyclidine Clozapine -0.316
## 32 Sams-Dodd 1998 74 Rat Phencyclidine Haloperidol -0.835
## 33 Sams-Dodd 1998 82 Rat Phencyclidine Haloperidol -1.405
## 34 Sams-Dodd 1998 90 Rat Phencyclidine Clozapine -0.969
## 35 Sams-Dodd 1998 97 Rat Phencyclidine Clozapine -0.058
## 36 Sams-Dodd 1998 75 Rat Phencyclidine Haloperidol -1.780
## 37 Sams-Dodd 1998 83 Rat Phencyclidine Haloperidol -0.710
## 38 Sams-Dodd 1998 76 Rat Phencyclidine Haloperidol -3.538
## 39 Sams-Dodd 1998 84 Rat Phencyclidine Haloperidol -0.788
## 40 Sams-Dodd 1998 98 Rat Phencyclidine Clozapine -0.303
## 41 Sams-Dodd 1998 91 Rat Phencyclidine Clozapine -1.386
## 42 Pouzet 2002 b 136 Rat Phencyclidine Risperidone -2.409
## 43 Pouzet 2002 b 137 Rat Phencyclidine Risperidone -5.003
## 44 Kaminska 2015 179 Rat MK-801 Risperidone -0.204
## 45 Kaminska 2015 185 Rat MK-801 Risperidone 0.101
## 46 Kaminska 2015 180 Rat MK-801 Risperidone 0.275
## 47 Maehara 2011 188 Rat MK-801 Clozapine -0.050
## 48 Maehara 2011 189 Rat MK-801 Clozapine -0.065
## 49 Gacsalyi 2013 212 Rat Phencyclidine Olanzapine -0.877
## 50 Neill 2016 225 Rat Phencyclidine Cariprazine -0.081
## 51 Neill 2016 226 Rat Phencyclidine Cariprazine -1.368
## 52 Neill 2016 228 Rat Phencyclidine Risperidone -0.164
## 53 Neill 2016 227 Rat Phencyclidine Cariprazine -2.230
## 54 Sams-Dodd 1996 233 Rat Phencyclidine Haloperidol 1.234
## 55 Sams-Dodd 1996 234 Rat Phencyclidine Haloperidol -2.592
## 56 Sams-Dodd 1996 243 Rat Phencyclidine Clozapine 0.293
## 57 Sams-Dodd 1996 244 Rat Phencyclidine Clozapine -1.177
## 58 Sams-Dodd 1996 239 Rat Phencyclidine Haloperidol 0.089
## 59 Sams-Dodd 1996 240 Rat Phencyclidine Haloperidol -1.150
## 60 Sams-Dodd 1996 241 Rat Phencyclidine Haloperidol -4.930
## 61 Sams-Dodd 1996 251 Rat Phencyclidine Clozapine -0.079
## 62 Sams-Dodd 1996 242 Rat Phencyclidine Haloperidol -2.926
## 63 Sams-Dodd 1996 252 Rat Phencyclidine Clozapine -1.260
## 64 Sams-Dodd 1996 253 Rat Phencyclidine Clozapine -1.533
## 65 Sams-Dodd 1996 254 Rat Phencyclidine Clozapine -2.002
## 66 Sams-Dodd 1998 d 259 Rat Phencyclidine Sulpiride -0.155
## 67 Sams-Dodd 1998 d 260 Rat Phencyclidine Sulpiride -0.550
## 68 Sams-Dodd 1998 d 261 Rat Phencyclidine Sulpiride -0.927
## 69 Sams-Dodd 1998 d 262 Rat Phencyclidine Sulpiride -2.223
## 70 Vijeepallam 2016 268 Mouse Ketamine Clozapine -0.111
## 71 Becker 2004 295 Rat Ketamine Risperidone -0.635
## 72 Becker 2004 292 Rat Ketamine Clozapine -8.925
## 73 Becker 2004 283 Rat Ketamine Haloperidol 0.155
## 74 Becker 2004 289 Rat Ketamine Risperidone -0.205
## 75 Becker 2004 286 Rat Ketamine Clozapine -0.668
## 76 Becker 2004 274 Rat Ketamine Haloperidol -0.461
## 77 Becker 2004 280 Rat Ketamine Risperidone -0.152
## 78 Becker 2004 277 Rat Ketamine Clozapine -0.835
## 79 Satow 2009 316 Rat MK-801 Haloperidol 0.295
## 80 Satow 2009 317 Rat MK-801 Haloperidol -2.190
## 81 Satow 2009 318 Rat MK-801 Haloperidol -1.077
## 82 Satow 2009 319 Rat MK-801 Haloperidol -2.979
## 83 Satow 2009 322 Rat MK-801 Clozapine 0.365
## 84 Satow 2009 323 Rat MK-801 Clozapine 0.361
## 85 Tarland 2017 334 Rat Phencyclidine Aripiprazole -2.021
## 86 Corbett 1995 375 Rat Phencyclidine Risperidone -0.251
## 87 Corbett 1995 367 Rat Phencyclidine Haloperidol -2.995
## 88 Corbett 1995 376 Rat Phencyclidine Risperidone -1.752
## 89 Corbett 1995 368 Rat Phencyclidine Haloperidol -4.675
## 90 Corbett 1995 383 Rat Phencyclidine Olanzapine -0.478
## 91 Corbett 1995 384 Rat Phencyclidine Olanzapine -1.805
## 92 Corbett 1995 371 Rat Phencyclidine Chlorpromazine 0.161
## 93 Corbett 1995 372 Rat Phencyclidine Chlorpromazine -1.283
## 94 Corbett 1995 379 Rat Phencyclidine Clozapine -1.165
## 95 Corbett 1995 380 Rat Phencyclidine Clozapine -1.063
## 96 Gururajan 2012 392 Rat MK-801 Clozapine -1.427
## 97 Gururajan 2012 393 Rat MK-801 Clozapine -0.696
## 98 Hereta 2019 404 Rat MK-801 Aripiprazole -0.804
## 99 Hereta 2019 405 Rat MK-801 Aripiprazole -0.316
## 100 Hereta 2019 410 Rat MK-801 Aripiprazole 0.543
## 101 Hereta 2019 413 Rat MK-801 Aripiprazole -0.794
## 102 Hereta 2019 406 Rat MK-801 Aripiprazole -1.058
## 103 Hereta 2019 407 Rat MK-801 Aripiprazole -0.719
## 104 Morimoto 2002 444 Rat MK-801 Risperidone -1.235
## 105 Morimoto 2002 445 Rat MK-801 Risperidone -0.147
## 106 Morimoto 2002 446 Rat MK-801 Risperidone -1.429
## 107 Morimoto 2002 447 Rat MK-801 Risperidone -3.258
## 108 Morimoto 2002 422 Rat MK-801 Clozapine -0.167
## 109 Morimoto 2002 423 Rat MK-801 Clozapine 0.893
## 110 Morimoto 2002 424 Rat MK-801 Clozapine 0.531
## 111 Morimoto 2002 425 Rat MK-801 Clozapine -1.141
## 112 Morimoto 2002 426 Rat MK-801 Clozapine -9.484
## 113 Morimoto 2002 431 Rat MK-801 Haloperidol -0.189
## 114 Morimoto 2002 432 Rat MK-801 Haloperidol -0.386
## 115 Morimoto 2002 433 Rat MK-801 Haloperidol -1.756
## 116 Morimoto 2002 434 Rat MK-801 Haloperidol -6.697
## 117 Rung 2005 b 463 Rat MK-801 Haloperidol -0.233
## 118 Rung 2005 b 464 Rat MK-801 Haloperidol -2.953
## 119 Rung 2005 b 465 Rat MK-801 Haloperidol -3.768
## 120 Rung 2005 b 469 Rat MK-801 Clozapine 0.115
## 121 Rung 2005 b 470 Rat MK-801 Clozapine 0.281
## 122 Rung 2005 b 471 Rat MK-801 Clozapine -0.102
## ci_lb ci_ub
## 1 -1.883 -0.032
## 2 -2.363 -0.411
## 3 -2.997 -0.873
## 4 -0.485 1.285
## 5 -1.759 0.071
## 6 -1.786 0.048
## 7 -1.076 0.532
## 8 -0.064 1.593
## 9 -2.508 -0.672
## 10 -1.594 0.064
## 11 -1.078 0.530
## 12 -1.214 0.402
## 13 -1.394 0.239
## 14 -0.896 0.705
## 15 -4.395 -1.983
## 16 -2.872 -0.942
## 17 -1.724 -0.047
## 18 -1.774 -0.089
## 19 -1.709 -0.035
## 20 -0.732 0.869
## 21 -2.543 -0.698
## 22 -1.641 0.023
## 23 -2.357 -0.557
## 24 -5.957 -2.968
## 25 -2.909 -0.969
## 26 -2.648 -0.443
## 27 -0.648 0.954
## 28 -0.937 0.665
## 29 -1.081 0.527
## 30 -1.059 0.547
## 31 -1.122 0.489
## 32 -1.670 -0.001
## 33 -2.299 -0.512
## 34 -1.815 -0.123
## 35 -0.858 0.743
## 36 -2.726 -0.835
## 37 -1.535 0.115
## 38 -4.819 -2.256
## 39 -1.618 0.043
## 40 -1.108 0.502
## 41 -2.277 -0.495
## 42 -3.895 -0.923
## 43 -7.302 -2.703
## 44 -1.339 0.930
## 45 -1.031 1.234
## 46 -0.862 1.412
## 47 -1.083 0.984
## 48 -1.099 0.968
## 49 -2.328 0.574
## 50 -1.062 0.899
## 51 -2.457 -0.280
## 52 -1.146 0.818
## 53 -3.478 -0.982
## 54 0.361 2.107
## 55 -3.677 -1.507
## 56 -0.511 1.097
## 57 -2.043 -0.310
## 58 -0.711 0.890
## 59 -2.013 -0.286
## 60 -6.538 -3.322
## 61 -0.879 0.722
## 62 -4.078 -1.775
## 63 -2.136 -0.384
## 64 -2.443 -0.623
## 65 -2.982 -1.022
## 66 -0.957 0.646
## 67 -1.366 0.265
## 68 -1.769 -0.085
## 69 -3.241 -1.205
## 70 -0.804 0.583
## 71 -1.538 0.268
## 72 -12.396 -5.454
## 73 -0.861 1.171
## 74 -1.064 0.653
## 75 -1.573 0.237
## 76 -1.328 0.407
## 77 -0.989 0.685
## 78 -1.864 0.195
## 79 -0.656 1.246
## 80 -3.345 -1.034
## 81 -1.958 -0.196
## 82 -4.555 -1.403
## 83 -0.776 1.506
## 84 -0.779 1.502
## 85 -3.308 -0.733
## 86 -1.388 0.885
## 87 -4.643 -1.347
## 88 -3.083 -0.421
## 89 -6.862 -2.489
## 90 -1.625 0.670
## 91 -3.148 -0.463
## 92 -0.973 1.294
## 93 -2.526 -0.041
## 94 -2.389 0.059
## 95 -2.272 0.146
## 96 -2.816 -0.039
## 97 -1.972 0.581
## 98 -1.981 0.372
## 99 -1.454 0.823
## 100 -0.609 1.695
## 101 -1.969 0.381
## 102 -2.267 0.150
## 103 -1.886 0.449
## 104 -2.588 0.117
## 105 -1.389 1.094
## 106 -2.818 -0.040
## 107 -5.149 -1.367
## 108 -1.409 1.075
## 109 -0.407 2.193
## 110 -0.731 1.792
## 111 -2.478 0.196
## 112 -13.822 -5.147
## 113 -1.431 1.053
## 114 -1.637 0.866
## 115 -3.215 -0.297
## 116 -9.883 -3.511
## 117 -1.161 0.694
## 118 -4.289 -1.617
## 119 -5.307 -2.229
## 120 -1.017 1.248
## 121 -0.856 1.419
## 122 -1.234 1.030Individual effect sizes included in the meta-analysis. This table lists all calculated Hedges’ g values and corresponding confidence intervals used in the analyses.
Session info
## R version 4.3.1 (2023-06-16 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 11 x64 (build 26100)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Portuguese_Brazil.utf8 LC_CTYPE=Portuguese_Brazil.utf8
## [3] LC_MONETARY=Portuguese_Brazil.utf8 LC_NUMERIC=C
## [5] LC_TIME=Portuguese_Brazil.utf8
##
## time zone: America/Sao_Paulo
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] RColorBrewer_1.1-3 scales_1.4.0 stringr_1.5.1
## [4] forcats_1.0.1 ggalluvial_0.12.5 tidyr_1.3.1
## [7] ggplot2_4.0.0 orchaRd_2.1.3 clubSandwich_0.6.1
## [10] metafor_4.8-0 numDeriv_2016.8-1.1 metadat_1.2-0
## [13] Matrix_1.6-5 dplyr_1.1.4 readxl_1.4.5
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 beeswarm_0.4.0 xfun_0.52 bslib_0.9.0
## [5] lattice_0.22-6 mathjaxr_1.6-0 vctrs_0.6.5 tools_4.3.1
## [9] generics_0.1.4 sandwich_3.1-1 tibble_3.2.1 pkgconfig_2.0.3
## [13] S7_0.2.0 lifecycle_1.0.4 compiler_4.3.1 farver_2.1.2
## [17] textshaping_1.0.0 prettydoc_0.4.1 codetools_0.2-20 vipor_0.4.7
## [21] htmltools_0.5.8.1 sass_0.4.9 yaml_2.3.10 pillar_1.11.1
## [25] jquerylib_0.1.4 MASS_7.3-60.0.1 cachem_1.1.0 multcomp_1.4-28
## [29] nlme_3.1-164 tidyselect_1.2.1 digest_0.6.35 mvtnorm_1.3-3
## [33] stringi_1.8.7 purrr_1.0.2 labeling_0.4.3 splines_4.3.1
## [37] latex2exp_0.9.6 fastmap_1.2.0 grid_4.3.1 cli_3.6.2
## [41] magrittr_2.0.3 survival_3.5-8 TH.data_1.1-4 withr_3.0.2
## [45] ggbeeswarm_0.7.2 estimability_1.5.1 rmarkdown_2.30 emmeans_1.11.2-8
## [49] cellranger_1.1.0 ragg_1.3.3 zoo_1.8-13 evaluate_1.0.5
## [53] knitr_1.50 rlang_1.1.5 xtable_1.8-4 glue_1.8.0
## [57] rstudioapi_0.17.1 jsonlite_2.0.0 R6_2.6.1 systemfonts_1.2.2